Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Automatically generating abstractions for planning
Artificial Intelligence
Downward refinement and the efficiency of hierarchical problem solving
Artificial Intelligence
Engineering and compiling planning domain models to promote validity and efficiency
Artificial Intelligence
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Sokoban: enhancing general single-agent search methods using domain knowledge
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
TALplanner: A temporal logic based forward chaining planner
Annals of Mathematics and Artificial Intelligence
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
Combining Macro-operators with Control Knowledge
Inductive Logic Programming
Robot task planning using semantic maps
Robotics and Autonomous Systems
Combining Domain-Independent Planning and HTN Planning: The Duet Planner
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
WoLLIC '09 Proceedings of the 16th International Workshop on Logic, Language, Information and Computation
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
The complexity of planning problems with simple causal graphs
Journal of Artificial Intelligence Research
Reducing accidental complexity in planning problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The role of macros in tractable planning
Journal of Artificial Intelligence Research
Integration of symmetry and macro-operators in planning
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Planning by guided hill-climbing
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
The Knowledge Engineering Review
Implicit Learning of Compiled Macro-Actions for Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Constraint Based Planning with Composable Substate Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Completeness-Preserving Pruning for Optimal Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Fast forward planning by guided enforced hill climbing
Engineering Applications of Artificial Intelligence
RECYCLE: Learning looping workflows from annotated traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Scaling up heuristic planning with relational decision trees
Journal of Artificial Intelligence Research
Speeding up planning through minimal generalizations of partially ordered plans
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
Macro learning in planning as parameter configuration
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Proximity-based non-uniform abstractions for approximate planning
Journal of Artificial Intelligence Research
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
Efficient search for transformation-based inference
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Evaluation of a simple, scalable, parallel best-first search strategy
Artificial Intelligence
Goal distance estimation for automated planning using neural networks and support vector machines
Natural Computing: an international journal
Refining incomplete planning domain models through plan traces
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.